Tumour growth prediction of follow‐up lung cancer via conditional recurrent variational autoencoder. Issue 15 (18th February 2021)

• Record Type: Journal Article
• Authors: Xiao, Ning; Qiang, Yan; Zhao, Zijuan; Zhao, Juanjuan; Lian, Jianhong
• Is Part Of: IET image processing. Volume 14:Issue 15(2020)
• Page Start: 3975
• Page End: 3981
• Publication Date: 2021-02-18
• Subjects: cancer -- image reconstruction -- tumours -- medical image processing -- recurrent neural nets -- image segmentation -- patient treatment -- lung -- computerised tomography -- physiological models; tumour growth prediction -- lung cancer -- conditional recurrent variational autoencoder -- lung tumour growth -- early treatment -- intuitive judgments -- clear judgments -- treatment opportunities -- recurrent neural networks -- recurrent units -- different tumour development varies -- personalised prediction -- total variation regularisation term -- cooperative hospital dataset -- lung tumours -- realistic tumour images; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0496
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The prediction of lung tumour growth is the key to early treatment of lung cancer. However, the lack of intuitive and clear judgments about the future development of the tumour often leads patients to miss the best treatment opportunities. Combining the characteristics of the variational autoencoder and recurrent neural networks, this study proposes a tumour growth prediction via a conditional recurrent variational autoencoder. The proposed model uses a variational autoencoder to reconstruct tumour images at different times. Meanwhile, the recurrent units are proposed to infer the relationship between tumour images according to the chronological order. The different tumour development varies in different patients, patients' condition is adopted to achieve personalised prediction. To solve the problem of blurred results, the authors add the total variation regularisation term into the object function. The proposed method was tested on longitudinal studies, National Lung Screening Trial and cooperative hospital dataset, with three points on lung tumours. The precision, recall, and dice similarity coefficient reach 82.22, 79.89 and 82.49%, respectively. Both quantitative and qualitative experimental results show that the proposed method can produce realistic tumour images.